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So, in essence, this network can learn to "unproject" images.

Since projection is a lossy operation, a projected image has potentially multiple inverses. And this makes me wonder how this system deals with the situation where two or more inverses exist and are equally likely.

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This is an interesting question. Technically, we capture a probability distribution in the code layer (between encoder and decoder). So you can sample from it multiple times and assess uncertainty. However, we have not really studied this.

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